NVIDIA

@nvidia source repo

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Flashdreams Integrations · nvidia
flashdreams integration architecture

2.2k repo stars
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Profile Model Performance · nvidia
Inspect and baseline performance for FlashDreams-style model integrations and interactive demos: map the generation path, add trustworthy timing splits, build focused probes, and identify whether decode, model/denoise, cache, data transfer, or presentation dominates. Use when starting performance work on an existing model runner, demo, serving path, or downstream integration before implementing speedups. Pair with `apply-inference-optimizations` after the bottleneck is known and `validate-performance-quality` for benchmark and quality gates.

2.2k repo stars
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Flashdreams Postprocessing · nvidia
Add or modify FlashDreams video post-processing processors, sessions, presets, and runner stream wiring. Use when implementing a new VideoPostProcessorConfig / VideoPostProcessor / VideoPostProcessorSession, registering a --postprocess.preset entry point, changing VideoPostprocessStream behavior, or reasoning about streaming buffering, layouts, per-view processing, distributed execution, or postprocess tests.

2.2k repo stars
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Validate Performance Quality · nvidia
Design benchmark, quality, and documentation validation for FlashDreams-style performance changes. Use when adding or updating sweep commands, profiler probes, decoder-quality comparisons, compile/cache probes, manual GPU validation, performance summaries, model cards, or README guidance after optimizing a model integration, demo, or serving path.

2.2k repo stars
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Apply Inference Optimizations · nvidia
Apply FlashDreams-style inference speedups to model integrations after a baseline exists: bounded windows and fixed K/V caches, cache/decode overlap, `torch.compile`, CUDA graph capture, attention backend checks, decoder layout or replacement, transfer/materialization changes, and ordered presentation tuning. Use when porting known optimizations into a runner, demo, serving adapter, or downstream integration while preserving quality and reset behavior.

2.2k repo stars
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Aicr Triage · nvidia
Use when the user runs `/aicr-triage` or asks to triage, review, or clean up a GitHub org-level Projects v2 board (default NVIDIA AICR project 248). Reviews active non-Done issues, then promotes P2 issues to P1, demotes Ready items to Backlog, closes superseded issues, and classifies unclassified ones — applying only user-confirmed changes via `gh` CLI. Triggers on backlog hygiene before sprint planning or release prep, or when the user files a new issue and asks to classify it on the board.

2.2k repo stars
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Aicr Uat Report · nvidia bundle
Use when reporting on UAT health across services and GPU targets — which service (EKS/GKE/AKS) x GPU (H100/GB200) x intent combinations are passing or failing in the UAT Run workflow (uat-run.yaml). Triggers on "UAT report", "/aicr-uat-report", "which UAT combos are failing", "UAT pass rate", "download the UAT debug bundle", "why did the UAT run fail", or RC/release-candidate validation prep that needs the combinations to test manually. Runs the bundled uat_report.py, classifies failures as product vs infra signal, prints a summary table plus an RC validation priority list, and can download the per-run cluster debug bundles for triage.

2.2k repo stars
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Aicr Cross Review · nvidia bundle
Multi-agent PR review using Claude Code, Codex, and CodeRabbit. Runs parallel reviews with integration impact analysis, then one cross-review round to a 2-of-3 consensus, with every confirmed finding adversarially verified by a fresh agent. Never runs the reviewed commit's code, and never posts unless explicitly asked. Use when asked for a thorough cross-review or multi-reviewer analysis. Requires the Codex plugin; CodeRabbit is best-effort. Claude Code only — uses the Workflow and Agent tools, which are not available in other agents.

2.2k repo stars
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Aicr Auditing Docs · nvidia
Use when reviewing AICR's Markdown documentation for duplication, drift, bloat, and gaps — to keep docs high-value as the project evolves. Triggers on "audit the docs", "review documentation", "docs cleanup", "/aicr-auditing-docs", or any request to find redundant/stale/missing docs across README, docs/, demos/, and the root governance files. Produces a prioritized findings report (research, not edits) grouped by the five audit dimensions, anchored to the project's canonical sources of truth.

2.2k repo stars
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Aicr Release Notes · nvidia
Use when drafting the human-readable GitHub release notes summary for an upcoming AICR release. Triggers on "release notes", "draft release notes", "/aicr-release-notes", or any request to summarize commits since the last tag into a polished release announcement. Runs tools/changelog, groups commits into thematic highlights, mirrors the style of the previous release, and writes a Markdown draft to a temp file for hand-editing before publishing.

2.2k repo stars
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Aicr Managing Openvex · nvidia
Use when adding, updating, or removing CVE/GHSA suppressions in `.openvex.json` — the OpenVEX document consumed by the weekly image vulnerability scan workflow. Triggers on "VEX", "OpenVEX", ".openvex.json", "suppress CVE", "ignore CVE", "vulnerability suppression", "aiperf-bench CVE", or any request to act on findings reported by `Weekly Image Vulnerability Scan` for the aiperf-bench image. Keeps the file current: adds reachability-evidenced statements for new HIGH+ findings, drops statements that no longer apply (dependency upgraded past the fix, advisory recalled, package removed), and verifies suppressions actually land in the JSON output.

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Aicr Analyzing Snapshots · nvidia
Use when analyzing an AICR snapshot YAML file, reviewing cluster state, comparing provider characteristics, extracting GPU/network topology insights, or generating a cluster assessment report from a snapshot. Triggers on: snapshot analysis, cluster review, provider comparison, GPU topology, node health, snapshot report.

2.2k repo stars
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Aicr Creating Slide Decks · nvidia bundle
Use when building a self-contained HTML slide deck or visual talking-point for a technical concept or workflow (e.g. a demos/*.html) — shown full-screen or projected and narrated, opening in any browser with no build step or dependencies.

2.2k repo stars
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Aicr Creating Guided Demos · nvidia bundle
Scaffolds an interactive guided demo script (demos/*.sh), live or self-paced, with the Frame → Tell → Show → Close pattern. Triggers on "demo script", "guided walkthrough", "demos/*.sh", "live demo".

2.2k repo stars
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K8S Launch Kit Clean · nvidia bundle
Remove an NVIDIA Network Operator deployment from a Kubernetes cluster with l8k clean. Use when the user explicitly asks to uninstall, tear down, remove, reset, or clean a Network Operator installation, delete its custom resources, or keep the Helm chart while clearing Network Operator CRs.

2.2k repo stars
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K8S Network Engineer · nvidia bundle
Embody a senior NVIDIA Networking Engineer who is an expert on deploying cloud-native networking on Kubernetes with k8s-launch-kit (l8k). Activate whenever the user mentions NVIDIA network profiles, SR-IOV, RDMA, Spectrum-X, BlueField, ConnectX, NIC configuration, Network Operator, DOCA drivers, multirail networking, l8k, k8s-launch-kit, or any Kubernetes networking topic involving NVIDIA hardware. Also activate when the user asks general questions about high-performance networking, GPU interconnect, or RDMA configuration.

2.2k repo stars
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K8S Launch Kit Config · nvidia bundle
Use this skill when the user needs help understanding, creating, or editing a k8s-launch-kit (l8k) configuration file (l8k-config.yaml or cluster-config.yaml). Activate for: config file questions, parameter tuning, subnet configuration, NV-IPAM setup, DOCA driver settings, maintenance concurrency, NIC configuration operator settings, changing MTU, VFs, resource names, or understanding what any config field does.

2.2k repo stars
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K8S Launch Kit Deploy · nvidia
Use this skill when the user wants to deploy generated NVIDIA networking manifests to a Kubernetes cluster using k8s-launch-kit (l8k). Activate for: applying manifests, deploying to cluster, the `l8k deploy` subcommand or the legacy --deploy flag on `l8k generate`, applying generated files, or any mention of pushing l8k output to a live cluster. Even if the user just says 'apply these' or 'push to cluster' after generating manifests, use this skill.

2.2k repo stars
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K8S Launch Kit Dryrun · nvidia
Use this skill when the user wants to preview what k8s-launch-kit (l8k) would deploy without making changes, or wants to safely validate their configuration before applying. Activate for: dry-run, preview, validation, 'what would happen if', testing configurations, schema discovery, checking generated manifests, or any cautious pre-deployment step. Also use when the user asks 'is my config valid' or 'show me what would be created' -- even without mentioning dry-run explicitly.

2.2k repo stars
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K8S Launch Kit Shared · nvidia
k8s-launch-kit (l8k) CLI: Shared patterns for binary location, global flags, output formatting, exit codes, and error handling. Read this before using any other k8s-launch-kit skill.

2.2k repo stars
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K8S Launch Kit Discover · nvidia bundle
Use this skill when the user wants to discover their Kubernetes cluster's network hardware capabilities using k8s-launch-kit (l8k). Activate for: cluster discovery, hardware detection, NIC detection, finding what GPUs or NICs are in a cluster, creating a cluster config file, or when the user says 'discover' in the context of l8k or NVIDIA networking.

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K8S Launch Kit Generate · nvidia bundle
Use this skill when the user wants to generate Kubernetes YAML manifests for NVIDIA networking deployment using k8s-launch-kit (l8k). Activate for: manifest generation, profile selection, choosing between SR-IOV/host-device/RDMA-shared/IPoIB/MacVLAN/Spectrum-X, creating deployment files, or when the user asks 'which profile should I use' or needs help choosing a network configuration.

2.2k repo stars
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K8S Launch Kit Pipeline · nvidia bundle
Use this skill when the user wants to run the full k8s-launch-kit (l8k) pipeline end-to-end: discover cluster hardware, select a profile, generate manifests, and deploy them all in one command. Also activate for CI/CD integration, automation pipelines, 'one-liner', 'complete workflow', or end-to-end NVIDIA networking deployment.

2.2k repo stars
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K8S Launch Kit Validate · nvidia
Use this skill when the user wants to verify that an NVIDIA networking deployment matches the configuration that produced it. Activate for: 'is my deployment correct', 'are all the manifests applied', 'does the network operator version match', 'verify deployment', 'check cluster state against config', or any question about whether the cluster reflects what l8k generated. Wraps the `l8k validate` subcommand.

2.2k repo stars
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K8S Launch Kit Troubleshoot · nvidia bundle
Use this skill when the user has problems with NVIDIA Network Operator on Kubernetes, or wants to analyze a sosreport diagnostic dump. Activate for: OFED driver crashes, SR-IOV pods failing, NicClusterPolicy errors, network operator pod issues, RDMA not working, NIC configuration failures, pods stuck in CrashLoopBackOff or ContainerCreating with network annotations, VF allocation issues, or when the user mentions 'troubleshoot', 'debug', 'sosreport', 'diagnose', or describes any NVIDIA networking failure -- even if they don't explicitly ask for troubleshooting.

2.2k repo stars
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Nvflare Autofl · nvidia bundle
Use for agent-assisted Auto-FL optimization of an existing NVFLARE job in simulation, POC, or production. Do not use for code conversion, diagnosis-only work, or deployment setup.

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Nvflare Orient · nvidia bundle
Route open-ended NVFLARE advice and only conversion requests whose preliminary source inspection reports unresolved or conflicting ownership; never load this skill merely to inspect a concrete conversion request before selecting its detected framework converter.

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Nvflare Shared · nvidia bundle
Internal NVFLARE conversion references and templates. Use only when another NVFLARE skill directs you to a shared workflow, policy, or asset.

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Nvflare Fed Stats · nvidia bundle
Compute federated statistics over tabular data (count, sum, mean, stddev, var, histogram, quantile, noise-protected min/max) and image data (count, failure_count, pixel-intensity histogram) across NVFLARE sites via FedStatsRecipe — automatic and non-interactive from the dataset, feature names (header or supplied), and optionally a README or notes declaring which statistics to compute; do not use for model training conversion, hierarchical statistics, deployment, POC/production lifecycle, or failed-job diagnosis.

2.2k repo stars
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Nvflare Diagnose Job · nvidia bundle
Use when the user asks why a reported NVFLARE job failure signal occurred: the job failed, stalled, timed out, lost clients, ended with EXECUTION_EXCEPTION, or produced suspicious errors. Diagnose in simulation, POC, or production by collecting bounded evidence and mapping failure patterns to recovery actions.

2.2k repo stars
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Nvflare Autofl Report · nvidia bundle
Generate a reproducible final report, literature-outcome synthesis, JSON summary, and refreshed progress plot for a stopped or interrupted NVFLARE Auto-FL campaign.

2.2k repo stars
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Nvflare Convert Pytorch · nvidia bundle
Convert existing plain or manual PyTorch training code into an NVFLARE federated job using Client API model exchange, local validation, and job export; use when the user names plain PyTorch or preliminary source inspection identifies one plain-PyTorch owner, and not for Lightning, other frameworks, deployment, POC/production lifecycle, or experiment workflows.

2.2k repo stars
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Nvflare Convert Lightning · nvidia bundle
Convert existing PyTorch Lightning training code into an NVFLARE federated job using the Lightning Client API patch, local validation, and job export; use only when the request names federated/NVFLARE conversion or asks multiple sites to train collaboratively while keeping each site's data local, and either names PyTorch Lightning or preliminary source inspection identifies one Lightning owner; do not use for non-federated Lightning work such as DDP, profiling, inference serving, or training-loop changes, nor for plain PyTorch, TensorFlow/Keras, other frameworks, deployment, POC/production lifecycle, or experiment workflows.

2.2k repo stars
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Nvflare Convert Huggingface · nvidia bundle
Convert existing Hugging Face Transformers Trainer or TRL SFTTrainer training code into an NVFLARE federated job using flare.patch(trainer), local validation, and job export; use when the user names Hugging Face or preliminary source inspection identifies one Hugging Face owner, and not for manual PyTorch loops, Lightning, inference-only pipelines, deployment, or experiment workflows.

2.2k repo stars
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Autofl Nvflare · nvidia bundle
Help coding agents work on an NVFlare-based Auto-FL harness that follows an autoresearch-style loop. Use when the user wants to create, edit, debug, or extend program.md, task folders such as tasks/cifar10/ and tasks/vlm_med/, task-local job.py, client.py, model.py, shared custom_aggregators.py, mutation policies, results.tsv logging, or coding-agent prompts for a bounded federated-learning research loop. This skill is specifically for NVFlare harness work where the Client API loop, DIFF upload contract, and NUM_STEPS_CURRENT_ROUND metadata must stay intact unless the user explicitly asks for a protocol upgrade.

2.2k repo stars
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Autofl Nvflare Report · nvidia bundle
Generate and commit a markdown report after an Auto-FL NVFlare autoresearch experiment has been manually stopped. Use when the user asks to summarize a stopped campaign, report achieved improvements, explain implemented literature-derived ideas and sources, refresh progress plots, capture pasted agent model/effort/cost context when available, or commit the final report and progress plot to the current experiment branch.

2.2k repo stars
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Nvalchemi Loss API · nvidia
How to use built-in loss functions and implement custom losses using the BaseLossFunction template-method pattern — residual types, per-atom normalization, masking, and graph-balanced reductions. Use when choosing or weighting energy, force, or stress objectives for training or fine-tuning, masking atoms or graphs out of the loss, or writing a custom loss term.

2.2k repo stars
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Nvalchemi Reporting · nvidia
How to add observability to nvalchemi dynamics and training workflows using ReportingOrchestrator, RichReporter, TensorBoardReporter, scalar extraction, custom reporter callbacks, and dynamics LoggingHook. Use when showing live progress, writing TensorBoard summaries, preserving dynamics CSV rows, adding rank-safe distributed reporting, previewing Rich dashboards, or deciding between logging and reporting for training or molecular dynamics runs.

2.2k repo stars
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Nvalchemi Zarr Perf · nvidia
Performance tuning for nvalchemi's Zarr-backed Reader, Dataset, and DataLoader pipeline. Use when configuring AtomicDataZarrReader, Dataset, DataLoader, ZarrWriteConfig, or nvalchemi-io-test for training/inference throughput, especially shuffled access, graph-like random access, fused prefetch, pinned memory, validation overhead, or Zarr chunk/shard choices.

2.2k repo stars
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Nvalchemi Distributed · nvidia
How to run domain-decomposed (multi-GPU) MLIP simulations with DomainParallel — choose between the halo and graph-partition strategies, author a distribution_spec so a bring-your-own model runs under domain decomposition, and write a custom dynamics integrator that stays correct across ranks.

2.2k repo stars
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Nvalchemi Fine Tuning · nvidia
How to fine-tune nvalchemi-compatible models with FineTuningStrategy, pretrained checkpoint initialization, module patches, trainable-parameter filters, conservative optimizer defaults, validation, restart checkpoints, and model-agnostic MACE, AIMNet2, custom BaseModelMixin, or PyTorch inputs. Use when adapting a pretrained MLIP (e.g. MACE-MP) to new reference data, freezing or patching submodules during training, or resuming an interrupted fine-tune from a checkpoint.

2.2k repo stars
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Nvalchemi Data Storage · nvidia
How to write, read, compose, and load atomic data using nvalchemi's composable Zarr-backed storage pipeline (Writer, Reader, Dataset, MultiDataset, DataLoader). Use when saving simulation outputs or trajectories to disk, converting structures (e.g. ASE / extxyz) into Zarr stores, assembling datasets for training or inference, or wiring a DataLoader to stream batches to the GPU.

2.2k repo stars
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Nvalchemi Dynamics API · nvidia
How to configure and run dynamics simulations, compose multi-stage pipelines (FusedStage, DistributedPipeline), use inflight batching, and manage data sinks. Use when writing any simulation script — molecular dynamics (NVE/NVT), structure relaxation or geometry optimization (e.g. FIRE), equation-of-state or adsorption scans — or orchestrating many structures through a batched GPU pipeline.

2.2k repo stars
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Nvalchemi Training API · nvidia
How to configure nvalchemi training workflows with TrainingStrategy, custom training functions, standalone or composed losses, loss-weight schedules, optimizer and scheduler configs, validation, hooks, restartable checkpoints, model-agnostic inputs, and scaling to multiple GPUs or nodes with DistributedManager and DDPHook. Use when training a model from scratch, setting up optimizers, schedulers, validation, or checkpointing, or scaling a run across GPUs or nodes (DDP); for adapting a pretrained model, see nvalchemi-fine-tuning.

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Nvalchemi Dynamics Hooks · nvidia
How to use and write dynamics hooks — callbacks that observe or modify batch state at specific points during each simulation step. Use when a simulation needs neighbor-list rebuilds, convergence checks or early stopping, temperature control, per-step logging or trajectory capture, or any custom per-step behavior attached to a dynamics run.

2.2k repo stars
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Nvalchemi Model Wrapping · nvidia
How to wrap an arbitrary MLIP (Machine Learning Interatomic Potential) using the BaseModelMixin interface to standardize inputs, outputs, and embeddings. Use when integrating a model such as MACE or AIMNet2 (e.g. MACEWrapper, loading pretrained checkpoints) so dynamics, training, or fine-tuning stages can call it, or when exposing energies, forces, or embeddings from a custom PyTorch model.

2.2k repo stars
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Nvalchemi Data Structures · nvidia
How to use AtomicData and Batch, the core graph-based data structures for representing atomic systems and batching them for GPU computation. Use when building systems from positions, cells, and atomic numbers, converting from ASE Atoms, batching or unbatching structures, reading per-atom vs per-graph tensors, or debugging shape, dtype, or device errors in model inputs.

2.2k repo stars
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Nvalchemi Dynamics Implementation · nvidia
How to implement a dynamics integrator by subclassing BaseDynamics and overriding pre_update() and post_update() methods. Use when creating a custom integrator, optimizer, or sampler that the built-in stages do not provide; for configuring existing dynamics, see nvalchemi-dynamics-api.

2.2k repo stars
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Run · nvidia
Start the nvcf-ui development server. Use when asked to run, start, or launch the app, or to verify a change works in the browser.

2.2k repo stars
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Codegen · nvidia
Run and manage the Orval code generation pipeline for API hooks, TypeScript models, and MSW mocks. Use when editing an OpenAPI spec, running task generate, adding or modifying MSW scenarios, troubleshooting generated code, or when generated hooks are missing or out of date.

2.2k repo stars
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Testing · nvidia
Writing frontend tests for the nvcf-ui app — Vitest, Testing Library, MSW, and project-specific render helpers. Use when creating test files, writing component tests, setting up MSW handlers in tests, deciding which render helper to use, or when the user is adding or modifying a UI component and hasn't mentioned tests yet (tests are required). Covers TypeScript/React only — Go backend tests follow standard Go patterns.

2.2k repo stars
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Nvca Chart Release · nvidia
Release NVCA Operator chart changes from the native monorepo source to the vendored Helm chart. Use when updating the vendored NVCA Operator chart, changing NVCA image refs, publishing helm-nvca-operator, or validating the chart against a self-managed control plane.

2.2k repo stars
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Nvcf Explore Stack · nvidia
Navigate and explain the NVCF self-hosted stack inside the monorepo. Maps helmfile releases to their charts, image-source subtrees, helm hooks, namespaces, and `needs:` dependency chains. Reads deploy/stacks/self-managed/helmfile.d/*.yaml.gotmpl and deploy/stacks/nvcf-compute-plane/helmfile.d/*.yaml.gotmpl as the source of truth for ordering and versions. Use when a user or developer asks "what deploys X", "what does X depend on", "what hooks run for X", "walk me through deployment order", "which subtree do I edit to change X", "what namespaces does the stack use", or mentions stack topology, helmfile stages, deployment DAG, or dependency map.

2.2k repo stars
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Documentation Style · nvidia
NVCF public docs style: short, plain, ASCII Markdown with no bold, emojis, em-dash, or en-dash. Use when editing public docs, READMEs, AGENTS.md, agent skills, plans, GitHub issues, or GitHub Pull Request descriptions.

2.2k repo stars
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Add Feature · nvidia
Add a new UI feature to the nvcf-ui app — folder structure, routing, data loading, lazy-loading, testing, and completion checklist. Use when creating a new feature folder, adding a page or view, wiring routes, building a new screen, or deciding whether to extend an existing feature vs create a new one. Also use when the user asks to build something new in the UI, even if they don't say "feature" explicitly.

2.2k repo stars
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Nvcf Self Managed CLI · nvidia bundle
Install, operate, and tear down self-hosted NVIDIA Cloud Functions (NVCF) deployments with nvcf-cli. Use for control-plane or compute-plane install, status checks, cluster registration, function deploy/invoke, task create/list/cancel/delete, API keys, admin tokens, JWKS rotation, failed-install diagnosis, and uninstall or down workflows. Trigger keywords: nvcf, nvcf-cli, self-hosted nvcf, self-managed nvcf, NVCFBackend, NVCA, NCP, ICMS, helmfile, control plane, compute plane, LLM function, OpenAI-compatible invocation, Responses API, embeddings, batch task, task monitor, cluster rotate, cluster delete, helm task, helm-based task, task secrets, update task secrets, retrieve task results, bulk fetch tasks, task results, NVCT, NVCT task, NVCT batch job.

2.2k repo stars
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Nvca Self Managed Install · nvidia
Install or validate the NVCA Operator chart against a self-managed NVCF control plane from the native monorepo. Use when the control plane comes from deploy/stacks/self-managed and NVCA must be installed with stack-derived image repository settings.

2.2k repo stars
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Nvca Values Customization · nvidia
Customize NVCA Operator Helm chart values in the native monorepo. Use when modifying vendored defaults, changing stack-derived install values, adding deployment-time overrides, or updating scripts under deploy/helm/nvca-operator.

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Nvcf Self Managed Installation · nvidia bundle
Install and operate NVCF self-hosted control-plane and separate compute-plane stacks. Covers Helmfile values and CLI profile installation flows, teardown, values overrides, pull secrets, and troubleshooting. Use for nvcf-self-managed-stack, nvcf-compute-plane-stack, split compute-plane installation, control-plane installation, CLI-generated control-plane profiles, Helmfile, self-managed, or self-hosted deployments. Do NOT use for local k3d environments; use the local k3d development workflow instead.

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Nvcf Self Managed Prerequisite · nvidia bundle
Install the prerequisites the NVCA operator / compute plane needs before nvcf-nvca-install can succeed: the operator tool nvcf-cli (required by the compute-plane stack's register-cluster step), KAI Scheduler (for the KAIScheduler feature gate), and the SMB CSI driver (for the sharedStorage Samba sidecar PVCs). The two cluster components are cloud-neutral helm installs at the NVCF-validated version pins; same install on AKS, EKS, GKE, k3d, or bare metal. Use when the user mentions NVCA prereqs, nvcf-cli, "nvcf-cli not found", ensure-nvcf-cli, register-cluster, KAI Scheduler, SMB CSI, csi-driver-smb, queue quotas, default-parent-queue, NVCA shared-storage PVCs stuck Pending, or asks how to prepare a cluster before installing the NVCA operator.

2.2k repo stars
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Profiling · nvidia
Capture performance traces using CPU ChromeTrace, Tracy, and Nsight Systems/NVTX for Kit-based applications (Isaac Sim, Isaac Lab, Kit SDK). Covers COLD/WARM/TRACY measurement separation, canonical Tracy capture sequence, last-resort force-kill handling, nsys profile commands, Kit profiler args, and lightweight export handoff to nsys-analyze. Use when running profiling captures, setting up trace collection, or troubleshooting capture failures. NOT for adding profiling zones (use profiling-api), deep trace analysis (use nsys-analyze), memory allocation profiling (use tracy-memory), or applying performance fixes (use perf-tuning).

2.2k repo stars
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Nvtx Python · nvidia bundle
Profile Python functions with NVTX in non-Kit environments (Isaac Lab 3.0+ standalone, any Python app without Carbonite). Uses a bundled PYTHONPATH-scoped sitecustomize.py with sys.setprofile hook, NVTX push/pop ranges, module include/exclude filtering, and Nsight Systems integration. Use when CARB_PROFILING_PYTHON doesn't work (no Kit/Carbonite runtime), when profiling standalone Isaac Lab scripts, or when you need per-function Python tracing in nsys captures outside Kit.

2.2k repo stars
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Perf Tuning · nvidia bundle
Resolve common Kit/Isaac Sim/Isaac Lab performance issues using specific settings and configuration changes. Covers PresentFrame stalls, resolveSamplerFeedback, headless mode, multi-GPU tradeoffs, DLSS/DLSS-G, PhysX tuning, RTX presets (isaaclab_performance/balanced/quality), viewport gizmos, HydraEngine waitIdle, fsWatcher overhead, and CPU governor. Use when profiling data shows a specific bottleneck and you need the fix, when someone asks "why is it slow" and you have Tracy/nsys evidence, or when tuning RTX settings for GPU-bound workloads. NOT for: initial triage (use diagnose-perf), capturing profiles (use profiling), or analyzing traces (use nsys-analyze).

2.2k repo stars
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Nsys Analyze · nvidia
Analyze profiling data from Kit-based apps. Covers Omniverse-specific NVTX zone interpretation, phase detection using sqlite3, Tracy Statistics/Range Limit analysis, csvexport fallback queries, and two-version comparison methodology. Use after capturing profiles with the profiling skill. NOT for capturing traces (use profiling), adding zones to code (use profiling-api), or applying fixes (use perf-tuning).

2.2k repo stars
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Tracy Memory · nvidia
Profile CPU and GPU memory allocations using Tracy in Kit-based applications after Tracy capture tooling is installed. Covers LD_PRELOAD setup for liballocwrapper.so, Kit memory-channel flags, capture binary isolation (unset LD_PRELOAD), tracy-update strip-test verification, Tracy Memory tab analysis, and debug symbol requirements. Use when investigating memory leaks, allocation hotspots, or VRAM growth in Isaac Sim, Isaac Lab, or Kit apps. Requires profiling capture setup plus install-profilers. NOT for generic trace capture (use profiling) or non-memory trace analysis (use nsys-analyze).

2.2k repo stars
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Diagnose Perf · nvidia
First-responder performance triage for Isaac Sim and Isaac Lab. Identifies bottleneck category (GPU-bound, CPU-bound, VRAM, loading) using nvidia-smi and system tools without profiling. Use when a user reports slow FPS, stuttering, high latency, or wants a quick health check before profiling. NOT for applying specific fixes (use perf-tuning), capturing traces (use profiling), or analyzing traces (use nsys-analyze).

2.2k repo stars
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Profiling API · nvidia
Add profiling zones, metrics, and annotations to Kit-based C++ and Python code. Covers Carbonite macros (CARB_PROFILE_ZONE, CARB_PROFILE_FUNCTION, GPU zones), Python profiler API (decorators, begin/end), profiler masks, channels, Tracy plot data, event annotations, and automatic Kit Python function capture (CARB_PROFILING_PYTHON). Use when a developer asks how to add profiling spans to Kit/Carbonite code, configure masks/channels, record custom Tracy plots, or annotate traces with event markers. NOT for capturing traces (use profiling), analyzing traces (use nsys-analyze), or non-Kit Python function tracing (use nvtx-python).

2.2k repo stars
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Install Isaaclab · nvidia
Install Isaac Lab for Isaac Sim-backed workflows or Isaac Lab 3.0+ kit-less/Newton workflows, then verify the setup. Use when the user asks to install, set up, or build Isaac Lab.

2.2k repo stars
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Install Isaacsim · nvidia
Install Isaac Sim via pip or source build. Covers Docker setup, verification, and common install issues. Use when the user asks to install, set up, or build Isaac Sim.

2.2k repo stars
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Install Profilers · nvidia
Install profiling tools for Isaac Sim / Isaac Lab / Kit-based applications. Covers Nsight Systems (`nsys` CLI), `sqlite3`, Tracy `csvexport`, canonical Tracy `capture`/`capture-release`, and `update` for memory strip tests, with optional `tracy-capture`/`tracy-update` aliases. Use when setting up a profiling environment, when nsys/sqlite3/csvexport/capture/update tools are missing, or before running profiling, nsys-analyze, or tracy-memory.

2.2k repo stars
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Benchmark Isaaclab · nvidia
Run Isaac Lab benchmark scripts and interpret their outputs. Covers RL training throughput, non-RL environment step FPS, camera/load/startup benchmarks, batch suites, parameter gotchas, output files, and JSON result structure. Use when the user asks to run or compare Isaac Lab benchmarks. NOT for RL convergence or policy-quality validation, profiling capture (use profiling), trace analysis (use nsys-analyze), or applying performance fixes (use perf-tuning).

2.2k repo stars
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Benchmark Isaacsim · nvidia
Run Isaac Sim benchmark scripts and interpret benchmark outputs. Covers camera, SDG, scene-loading, robot, lidar/radar/sensor benchmark scripts, common parameters, output files, and benchmark-specific pitfalls. Use when the user asks to run or compare Isaac Sim benchmark results. NOT for initial bottleneck triage (use diagnose-perf), profiling capture (use profiling), trace analysis (use nsys-analyze), or applying performance fixes (use perf-tuning).

2.2k repo stars
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Kit App Streaming Debug · nvidia
Use when investigating Kit app livestream performance bottlenecks, WebRTC/native StreamSDK lag, freezes, dropped frames, browser WebRTC stats, copy fence timeouts, NVST_R_BUSY, disconnects, or resolution mismatch warnings in omni.kit.livestream.

2.2k repo stars
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Vllm Setup · nvidia
Deploy a vLLM inference server on an NVIDIA DGX Station GB300 with validated container, GPU targeting, and tuning parameters. Use when the user asks to serve a model with vLLM, start a vLLM endpoint, or set up OpenAI-compatible inference on DGX Station.

2.2k repo stars
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Dgx Station · nvidia bundle
Inspect and guide NVIDIA DGX Station GB300 development using the local dgx-assist CLI and pinned NVIDIA playbooks. Use for general Station platform questions, Software 1.0 or 2.0 compatibility, GB300 or RTX GPU selection, UUID ordering, mixed ATS/HMM coherency, CDMM, general containers, CDI, CUDA visibility, or vsloshd power-sloshing behavior. Do not use for vLLM or SGLang container selection or tuning, serving a named model, changing MIG, or troubleshooting a reported failure when the dedicated Station skill applies.

2.2k repo stars
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Dgx Diagnose · nvidia
Diagnose common DGX Station GB300 issues — CUDA crashes, wrong-GPU targeting, vLLM/SGLang container bugs, MIG state problems, NVLink/Fabric Manager errors, X/Vulkan failures, HuggingFace auth, and port conflicts. Use when the user reports a GPU error, inference server crash, MIG problem, or any unexplained DGX Station failure.

2.2k repo stars
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Sglang Setup · nvidia
Deploy an SGLang inference server on an NVIDIA DGX Station GB300 with the cu130 container, RadixAttention prefix caching, and structured JSON output support. Use when the user asks to serve a model with SGLang, start an SGLang endpoint, or needs structured-output inference on DGX Station.

2.2k repo stars
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Mig Configure · nvidia
Configure NVIDIA MIG (Multi-Instance GPU) partitions on the DGX Station GB300, including enabling MIG mode, choosing a profile layout, creating instances, and retrieving MIG UUIDs. Use when the user asks to partition the GB300, set up MIG, run multiple models in isolation on one GPU, or reconfigure existing MIG instances.

2.2k repo stars
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Dgx Station Mig · nvidia bundle
Inspect NVIDIA DGX Station GB300 MIG state and installed-driver profiles, create a digest-bound layout plan, disclose disruption and restoration, and apply an approved still-valid plan. Use when the user asks to enable, disable, partition, reconfigure, inspect, or troubleshoot MIG instances or needs MIG UUIDs. Never assume static profile IDs or terminate GPU clients.

2.2k repo stars
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Fhir Basics · nvidia
Teaches agents how FHIR R4 APIs work, what resources are available, how to query them with search parameters, and how to correctly parse all response formats including component Observations.

2.2k repo stars
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Case Summary · nvidia
Prepare a complete clinical case summary for a patient from FHIR endpoints. Use when asked to summarize a patient, compile a case, or prepare for tumor board.

2.2k repo stars
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Dgx Station Diagnose · nvidia bundle
Run and interpret the complete read-only dgx-assist diagnostic suite for NVIDIA DGX Station GB300, correlate findings with pinned NVIDIA playbooks, export a redacted support bundle, and apply one separately approved allowlisted fix. Use when the user reports a Station, CUDA, GPU health, coherency, vsloshd, Docker, CDI, MIG, cache, port, or owned inference-service failure.

2.2k repo stars
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Molecular Viz · nvidia
Visualize drug-protein complexes using build_viewer.py, PubChem, and OpenFold3 NIM. Use when asked to show a molecular structure, drug target, or protein visualization.

2.2k repo stars
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Dgx Station Inference · nvidia bundle
Resolve, tune, preflight, launch, verify, inspect, and stop exact-model inference on NVIDIA DGX Station through dgx-assist. Use for vLLM or SGLang container selection, NGC versus upstream, GPU memory utilization, CPU or KV offload, HBM fit, KV-cache sizing, ISL or context length, prefix caching, chunked prefill, batching, concurrency, performance tuning, serving or deploying a named model, an OpenAI-compatible endpoint, Station recipe models, or an owned inference service. Require an exact model ID for recipe resolution or model-specific tuning, and never recommend or substitute a different model.

2.2k repo stars
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Cohort Compare · nvidia
Analyze a cohort of patients from FHIR endpoints to find care gaps and patterns. Use when asked to compare patients, find quality gaps, or analyze a population.

2.2k repo stars
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Analysis Methods · nvidia bundle
Teaches the analyst agent how to write correct, robust Python analysis code for FHIR clinical data using pandas, matplotlib, and scipy.

2.2k repo stars
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Clinical Knowledge · nvidia
Teaches agents clinical reference ranges, condition codes, quality measure definitions, drug classifications, and regulatory context so they can flag abnormal values and identify care gaps.

2.2k repo stars
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Clinical Delegation · nvidia
How to delegate clinical tasks to specialist agents. Always use sub-agent runtime with explicit agentId — never ACP. Never call FHIR via web_fetch.

2.2k repo stars
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Ptq · nvidia bundle
Use when the user asks to "quantize a model", "run PTQ", "post-training quantization", "NVFP4 quantization", "FP8 quantization", "INT8 quantization", "INT4 AWQ", "quantize LLM", "quantize MoE", "quantize VLM", or needs to produce a quantized HuggingFace checkpoint from a pretrained model using ModelOpt. Do NOT use for multi-candidate recipe exploration or optimization (use quant-recipe-search).

2.2k repo stars
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Qad · nvidia
Run explicitly requested ModelOpt Quantization-Aware Distillation (QAD) on Slurm through Megatron Bridge to recover a measured BF16-to-PTQ accuracy gap. Use only when the user explicitly asks for QAD, including its topology, data preparation, Slurm launch, resume, checkpoint export, or recovery decisions.

2.2k repo stars
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Debug · nvidia
Run commands inside a remote Docker container via the file-based command relay (tools/debugger). Use when the user says "run in Docker", "run on GPU", "debug remotely", "run test in container", "check nvidia-smi", "run pytest in Docker", or needs to execute any command inside a Docker container that shares the repo filesystem. Requires the user to have started server.sh inside the container first.

2.2k repo stars
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Common · nvidia bundle
Shared ModelOpt support files. Use only when another ModelOpt skill directs you here.

2.2k repo stars
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Monitor · nvidia
Monitor submitted jobs (PTQ, evaluation, deployment) on SLURM clusters. Use when the user asks "check job status", "is my job done", "monitor my evaluation", "what's the status of the PTQ", "check on job <slurm_job_id>", or after any skill submits a long-running job. Also triggers on "nel status", "squeue", or any request to check progress of a previously submitted job.

2.2k repo stars
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Deployment · nvidia bundle
Serve a quantized or unquantized LLM checkpoint as an OpenAI-compatible API endpoint using vLLM, SGLang, or TRT-LLM. Use when user says "deploy model", "serve model", "start vLLM server", "launch SGLang", "TRT-LLM deploy", "AutoDeploy", "benchmark throughput", "serve checkpoint", or needs an inference endpoint from a HuggingFace or ModelOpt-quantized checkpoint. Do NOT use for quantizing models (use ptq) or evaluating accuracy (use evaluation).

2.2k repo stars
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Evaluation · nvidia bundle
Evaluates accuracy of quantized or unquantized LLMs using NeMo Evaluator Launcher (NEL). Triggers on "evaluate model", "benchmark accuracy", "run MMLU", "evaluate quantized model", "run nel". Handles deployment, config generation, and evaluation execution. Not for quantizing models (use ptq), deploying/serving models (use deployment), or comparing completed baseline-vs-quantized results (use compare-results).

2.2k repo stars
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Day0 Release · nvidia bundle
Deterministic end-to-end driver for day-0 quantized-checkpoint releases — chains PTQ → evaluation → comparison with enforced gates between stages (the evaluation stage deploys the checkpoint itself), and returns a publish decision (ACCEPT / REGRESSION / ANOMALOUS / INFEASIBLE). Use when the user asks to "release a model at day-0", "quantize and validate model X is within N% of baseline and tell me if it's publishable", or "run the full day-0 workflow". Do NOT use for single-stage requests — quantizing only (use ptq), serving only (use deployment), evaluating only (use evaluation), or comparing two existing runs (use compare-results).

2.2k repo stars
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Eagle3 Triage · nvidia
Triage a failed EAGLE3 pipeline run. Identifies which step failed (data synthesis, hidden state dump, training, or benchmark), diagnoses root cause from logs, and suggests fixes. Use when user reports an EAGLE3 pipeline failure or asks why a specific step failed. Also helps debug new model support issues.

2.2k repo stars
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Compare Results · nvidia bundle
Establish baseline-vs-candidate evaluation plans, delegate missing evaluations, compare validated results, and decide quantization feasibility. Use when the user asks to compare baseline vs quantized runs, explain an accuracy drop/regression, verify whether a quantized checkpoint is acceptable, or compare NEL/MLflow evaluation outputs. Do NOT use for generic single-model evaluation without comparison intent (use evaluation), live NEL status/debugging (use launching-evals), or generic MLflow browsing without a comparison goal (use accessing-mlflow).

2.2k repo stars
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Eagle3 Validate · nvidia
Validate that an EAGLE3 pipeline run completed successfully end-to-end. Checks all 4 steps produced expected artifacts, verifies acceptance rate meets threshold (>= 2.1), and produces a summary report. Use when user wants to verify a pipeline run or check benchmark results.

2.2k repo stars
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Launching Evals · nvidia bundle
Run, monitor, analyze, and debug LLM evaluations via nemo-evaluator-launcher. Covers running evaluations, checking status and live progress, debugging failed runs, exporting artifacts and logs, and analyzing results. ALWAYS triggers on mentions of running evaluations, checking progress, debugging failed evals, analyzing or analysing runs or results, run directories or artifact paths on clusters, Slurm job issues, invocation IDs, or inspecting logs (client logs, server logs, SSH to cluster, tail logs, grep logs). Do NOT use for creating or modifying evaluation configs.

2.2k repo stars

962 published skills · page 4 of 10